Section 15 Measure Classification
15.1 Introduction
Our measures are too loosely grouped, making it difficult for our optimization. We are aiming to have around 500 measures, currently we have more.
We will need our HRU ids spatially, as well as our Measures, erosion classes, and property register.
## Loading required package: sf
## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
## Loading required package: mapview
## Loading required package: DT
Get our maps:
# For measure IDs and location:
lu_map <- read_sf("model_data/input/land/CS10_LU.shp")
# For HRU IDs:
hru_map <- read_sf("model_data/cs10_setup/optain-cs10/data/vector/hru.shp")
# For Erosion Risk Levels:
erosion_map <- read_sf("model_data/input/soil/erosionriskclasses_kraakstad.shp")
# For Farm ownership IDs:
farm_map <- read_sf("model_data/input/property/matrikkel.shp")
farm_map <- st_transform(farm_map, st_crs(lu_map))
st_crs(lu_map) == st_crs(hru_map)## [1] TRUE
## [1] TRUE
## [1] TRUE
# filter data
lu_filter <- lu_map %>% select("buffer_6m_", "gully", "wetland", "dam", "type", "geometry")
hru_filter <- hru_map %>% select("name", "type")
erosion_filter <- erosion_map %>% select("A_HPKL", "geometry")
farm_filter <- farm_map %>% select("GARDSNUMME", "geometry")
farm_dissolved <- farm_filter %>%
group_by(GARDSNUMME) %>%
summarise()
erosion_dissolved <- erosion_filter %>%
group_by(A_HPKL) %>%
summarise()